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1 Recommendations for Equitable Allocation of Trades in High Frequency Trading Environments 1 John McPartland July 10, Executive Summary Most industry observers and much of the academic research in this area have concluded that high frequency trading (HFT) is generally beneficial. Many institutional investors, however, argue that HFT places them at a competitive disadvantage. 3 Digital computers will always have some structural (speed) advantages over human traders. This is inevitable. This paper 1) acknowledges and summarizes much of the relevant published research, 4 2) discusses some of the HFT strategies that likely run counter to good public policy, and 3) makes nine recommendations that, if implemented, would likely restore the perception of fairness and balance to market 1 The author is a senior policy advisor in the Financial Markets Group of the Federal Reserve Bank of Chicago. He wishes to acknowledge the very significant contributions that David Marshall and Rajeev Ranjan made to this paper. He also wishes to thank the many industry professionals who reviewed the document and its predecessor prior to publication. Any opinions expressed in this paper are those of the author, and those opinions do not necessarily reflect the opinions of the Federal Reserve Bank of Chicago or the opinions of the Federal Reserve System. 2 This policy paper expands upon the identically titled prior work, dated July 25, Andrew M. Brooks, 2012, Computerized trading: What should the rules of the road be?, testimony of vice president and head of U.S. equity trading, T. Rowe Price Associates, Inc., before the United States Senate, Committee on Banking, Housing, and Urban Affairs, Subcommittee on Securities, Insurance, and Investment, September 20, available at ae54-45ab-82fa-072c3ee7236f. See also Charles Schwab Corporation, 2014, High-frequency trading is a growing cancer that needs to be addressed, company statement, San Francisco, April 3, available at 4 See, for example, Anton Golub, 2011, Overview of high frequency trading, Manchester Business School, April 15, and Investment Industry Regulatory Organization of Canada, 2012, The HOT Study: Phases I and II of IIROC s study of high frequency trading activity on Canadian equity marketplaces, report, Toronto, December 12. 1

2 participants that would be willing to expose their resting orders to market risk for more than fleeting milliseconds. Readers should avoid the tendency to review this working paper only within the framework of their own nationality and market domain. The paper is meant to be global in scope. Some HFT practices that may be inappropriate (or banned) in some markets in some countries are alive and well in other markets in other countries. An exceptionally abbreviated summary of the nine recommendations follows. 1. Where appropriate, utilize a new trade allocation formula that is intermediate between the Pro Rata trade allocation formula and the Price/Time or FIFO (First In, First Out) trade allocation formula. 2. Create a new, optional, term limit order type that, as part of the trade allocation process, would reward traders for the time that their orders are committed to be resting in the order book. 3. Completely dark orders or the hidden portion of resting orders that are not fully displayed (lit) in the order book should go to the very end of the queue (within limit price) with respect to trade allocation. 4. Prior to trade allocation, multiple small orders from the same legal entity entered contemporaneously for the sole purpose of exploiting the rounding conventions of a trading venue should first be aggregated as a single order and should carry the lowest allocation priority time stamp of all of the orders so aggregated. 5. Rather than running a continuous trade match, trading venues should divide their trading sessions into discrete periods of one half second. At a completely random time within each half second period, the singleprice market-opening trade match and trade allocation algorithms should be run once. 6. Visibility into the order book should be no more granular than aggregate size at each price point. Market participants should not be able to view the size of individual orders or any other identifiers of any orders of others. This more granular information is not information that any market participant needs to make a fully informed economic decision as to the instantaneous value of the financial instrument being traded. 7. Under normal operating conditions, no market participant should be permitted to cancel an order before first obtaining an acknowledgement 2

3 from the trading venue that the original order was received. 5 We can envision no legitimate trading strategy where the practice of cancelling an order in this way would be necessary and any number of intentionally deceptive trading strategies where it would. 8. Each automated trading system (each individual algorithm) that has the capacity to generate, modify, or cancel orders without human intervention should have a unique identifier. That unique identifier must be known to every trading venue where the trading system can direct, modify, or cancel an order. Trading venues must ascribe the unique identifier as a critical information element of all relevant orders and matched trades throughout the audit trail. 9. Relevant authorities should assess and, if appropriate, seek public comment on precisely when trade information becomes generally available to the public at large. Organizations that colocate in the data centers of trading venues should not be receiving trade information from the trade match engines but should be receiving such information from the same ticker plants from which the general public receives trade information. The issue is whether some firms have access to and can trade on information that has not yet reached the public domain. Background Some twentieth-century financial markets had their origins in physical trading halls. The design of the physical trading floors and the rules of these exchanges provided the exchange members with a time, place, and informational advantage over the order flow. In turn, members, specialists, or market makers were expected to maintain continuous auction markets (presumptive responsibility 6 ). By the 1990s, open outcry markets had largely given way to modern screen-based electronic markets so-called click trading. Before click trading had largely given way to today s automated markets, no single class of market participants had a time, place, or informational advantage over all other classes of market participants. All market participants 5 This assumes that the trading venue is not experiencing technical difficulties that would prevent it from promptly sending drop copy confirmations to market participants, confirming receipt of orders. 6 U.S. Commodity Futures Trading Commission, Technology Advisory Committee, Market Access Subcommittee, 2002, Best practices for organized electronic markets, final report, Washington, DC, April, p. 4. 3

4 enjoyed an equal opportunity to buy at the bid and sell at the offer and to do so anonymously. As algorithmic trading became far more prevalent, investment managers increasingly discovered that the market neutrality of the click trading era had been lost; that today s algorithmic traders, or algo traders had assumed a dominant market making role; and that role and its twenty-first-century version of presumptive responsibilities came with a time, place, and informational advantage. While some investment managers might have thought that the phenomenon of market neutrality had been taken from them, market neutrality was never theirs in the first place. Algorithmic trading is quite simply more competitive, and it has changed the landscape and structure (and the public perception) of today s modern financial markets. In some sense, today s perception that today s markets may be unfair seems to be associated with the loss of the market neutrality that was present during the click trading era. Many industry observers seem to believe that HFT offers many benefits to organized financial markets and to society, including improved liquidity, tightened bid/ask spreads, and a decrease in intraday price volatility. This working paper describes some of the HFT techniques that have developed in electronic markets around the world, as well as their effects. Different financial centers have different rules and regulations regarding the appropriateness of some HFT techniques. This working paper is intended to be global in its scope and in its recommendations. All of its nine recommendations might not be appropriate for every electronic trading venue in every financial center. Throughout the working paper, when discussing different trade allocation methodologies, we refer to shares, futures, and lots, which are three terms we use interchangeably. Review of the Academic Literature 7 Brogaard, Hendershott, and Riordan (2013) analyzed NASDAQ and NYSE high frequency trading data 8 that show high frequency traders increase price 7 See Investment Industry Regulatory Organization of Canada (2012, appendix A, pp ). 8 The HFT data represent a sample of 120 randomly selected stocks listed on NASDAQ and NYSE for all of 2008 and Trades are time-stamped to the millisecond and identify the liquidity demander and supplier as a high frequency trader or non-high-frequency trader. 4

5 efficiency by trading in the same direction of permanent price changes and trading in the opposite direction of transitory pricing errors on normal trading days and on days with the highest price volatility. In contrast, liquiditysupplying nonmarketable orders executed via HFT are adversely selected in terms of the permanent and transitory components as these trades are in the direction opposite to permanent price changes and in the same direction as transitory pricing errors. HFT predicts price changes in the overall market over short horizons measured in seconds. HFT is correlated with public information, such as macro news announcements, marketwide price movements, and limit order book imbalances. 9 Jones (2013) notes that the volume of HFT has increased sharply over the past several years, has reduced trading costs, and has steadily improved liquidity. The main positive is that HFT can intermediate trades at lower cost. However, HFT speed could disadvantage other investors, and the resulting adverse selection could reduce market quality. Ideally, research in this area should attempt to determine the incremental effect of HFT beyond other structural and technological changes in equity markets. The best papers for this purpose attempt to isolate market structure changes that facilitate HFT. Virtually every time a market structure change results in more HFT, liquidity and market quality have improved because liquidity suppliers are better able to adjust their quotes in response to new information. Jones cites the concern that HFT may not help to stabilize prices during unusually volatile periods and notes that there is a potential for an unproductive arms race among HFT firms for speed. 10 Cartea and Panalva (2012) conclude that the presence of high frequency traders increases the price impact of liquidity trades and that this price impact increases as the size of the trades increase. High frequency traders increase microstructure noise of prices and increase trading volume. High frequency traders and non-high-frequency professional traders coexist as competition drives down profits for new HFT entrants while the presence of high frequency traders does not drive out traditional professional traders. Finally, the paper concludes that high frequency traders clearly generate costs, but they also 9 Jonathan Brogaard, Terrence Hendershott, and Ryan Riordan, 2013, High frequency trading and price discovery, University of Washington, University of California, Berkeley and University of Ontario Institute of Technology, working paper, April Charles M. Jones, 2013, What do we know about high-frequency trading?, Columbia Business School, research paper, No , March 20. 5

6 generate benefits, and that the net effect requires more precise empirical analysis. 11 The Litzenberger et al. (2010) paper concludes that overall market quality has improved significantly, including bid/ask spreads, liquidity, and transitory price impacts (measured by short-term variance ratios). Studies using proprietary, exchange-provided data that identify the trades of high frequency trading firms show that HFT firms contributed directly to narrowing bid/ask spreads, increasing liquidity, and reducing intraday transitory pricing errors and intraday volatility. 12 Wah and Wellman (2013) evaluate allocative efficiency and market liquidity arising from simulated order streams in fragmented financial markets. They find that market fragmentation and the presence of a latency arbitrageur reduce total surplus and impact liquidity negatively. By replacing continuous trade matching with periodic batch auctions or call markets, latency arbitrage opportunities are eliminated and further efficiencies are achieved by aggregating orders over short time periods. 13 Budish, Cramton, and Shim (2013) use actual millisecond quotation data to show that the prices of related financial instruments are highly correlated at human-scale time horizons but that these correlations break down completely at the single-digit millisecond level. The lack of price correlation at the millisecond level can be arbitraged away profitably if a market participant can act faster than other market participants similarly engaged in latency arbitrage. Their theoretical model shows that that quest for speed is not only wasteful but can lead to wider bid/ask spreads and thinner markets for fundamental investors than would be otherwise. They then use their model to show that frequent batch auctions can reduce the value of tiny speed advantages because it forces completion that was previously based on speed into competition to be based on price instead. They conclude that frequent 11 Álvaro Cartea and José Penalva, 2012, Where is the value in high frequency trading?, University College London and Universidad Carlos III, Madrid, working paper, February Robert Litzenberger, Jeff Castura, Richard Gorelick, and Yogesh Dwivedi, 2010, Market efficiency and microstructure evolution in U.S. equity markets: A high-frequency perspective, RGM Advisors LLC, working paper, August Elaine Wah and Michael P. Wellman, 2013, Latency arbitrage, market fragmentation, and efficiency: A two-market model, EC 13: Proceedings of the 14th ACM Conference on Electronic Commerce, New York: ACM, Inc., pp

7 batch auctions can lead to narrower bid/ask spreads, deeper markets, and greater social welfare 14 Questionable HFT Techniques Notwithstanding the evident benefits of HFT in electronic markets, many market participants have argued that some HFT practitioners utilize trading techniques that are detrimental to the well-functioning of financial markets. 15 Some of the trading techniques generally considered to be detrimental and not capital formative are spoofing, layering, and quote stuffing. 16 Spoofing and layering are not at all unique to HFT. Both almost always involve feigning to be a buyer when one is really a seller or vice versa. Algorithmic HFT has, however, allowed these two strategies to be taken to new levels. FINRA states the following about spoofing and layering: Generally, spoofing is a form of market manipulation which involves placing certain nonbona fide order(s), usually inside the existing National Best Bid or Offer (NBBO), with the intention of triggering another market participant(s) to join or improve the NBBO, followed by canceling the non-bona fide order, and entering an order on the opposite side of the market. Layering involves the placement of multiple, non-bona fide, limit orders on one side of the market at various price levels at or away from the NBBO to create the appearance of a change in the levels of supply and demand, thereby artificially moving the price of the security. An order is then executed on the opposite side of the market at the artificially created price, and the non-bona fide orders are immediately canceled Eric Budish, Peter Cramton, and John Shim, 2013, The high-frequency trading arms race: Frequent batch auctions as a market design response, University of Chicago Booth School of Business and University of Maryland, working paper, December See, for example, German Federal Ministry of Finance, Speed limit for high-frequency trading Federal government adopts legislation to avoid risks and prevent abuse in highfrequency trading, press release, Berlin, September 26, available at and Australian Securities & Investments Commission (ASIC), 2012, Australian market structure: Draft market integrity rules and guidance on automated trading, consultation paper, No. 84, Victoria, Australia, available at pdf/$file/cp184-published-13-August-2012.pdf. 16 See the proposed amendments to the ASIC Market Integrity Rules (ASX Market) to preclude market misconduct, manipulation or false trading in Australian Securities & Investments Commission, 2013, Dark liquidity and high-frequency trading, report, No. 331, Victoria, Australia, March, p Financial Industry Regulatory Authority (FINRA), 2012, FINRA joins exchanges and the SEC in fining Hold Brothers more than $5.9 million for manipulative trading, anti-money laundering, 7

8 Quote stuffing is unique to algorithmic HFT. Regarding this third dubious technique, Egginton, Van Ness, and Van Ness (2012) state the following: Quote stuffing is a practice in which a large number of orders to buy or sell securities are placed and then canceled almost immediately. During periods of intense quoting activity stocks experience decreased liquidity, higher trading costs, and increased short term volatility. 18 Imagine that you are bidding at an art auction, and the serious bidders are now reduced to two or three. One of the persons pretending to be an interested bidder is really the owner of the art piece currently being auctioned off. It is to their advantage to get the bona fide bidders to pay as much as possible for their art piece. Bidders indicate their willingness to bid to the auctioneer by raising the bidder numbers assigned to them by the auction house. The spoofing equivalent in this physical environment would be if the seller of the art piece, pretending to be a buyer, raised his or her bidder number one last time, solely to get the last remaining buyer to pay more than they otherwise would be willing to pay. Granted, the spoofer in this case is absolutely at risk of buying their own art piece unless their spoofing strategy is successful and a bona fide bidder betters the spoofer s bid. While the practice of allowing sellers to masquerade as buyers is probably not allowed at proper art auctions, its electronic equivalent is permitted and well practiced among some HFT practitioners. Make no mistake, HFT spoofers bids and offers are exposed to market risk as much as the bids and offers of click traders, even if they are often so exposed for only milliseconds. Spoofing is intentionally designed to be deceptive and, at a minimum, frustrates fair value investors ability to determine the true market value of the instruments that are being traded. Layering is only a slightly different technique, designed to similarly deceive market participants perception of the aggregate size of the bids and offers in the order book. By entering thousands of bids or offers, and then cancelling them virtually immediately, but only after they have been acknowledged as having been present in the order book, HFT practitioners can successfully create the illusion of greater size at the bid (or offer) than is realistically executable. Investment managers often refer to this phenomenon as phantom liquidity as the visible liquidity is often not there when one goes to hit the bid and other violations, press release, Washington, DC, available at 18 Jared Egginton, Bonnie Van Ness, and Robert Van Ness, 2012, Quote stuffing, Louisiana Tech University and University of Mississippi, March 15. 8

9 or lift the offer. Not unlike the massive white clouds in the sky, they are actually nothing more than thin water vapor that simply gives the illusion of being huge, massive objects. Frequently, high frequency traders layer quotes on the bid side of the market, in an attempt to attract other bidders, and then hit the bid side of the market as a seller in size. Layering is designed to be intentionally deceptive and similarly frustrates fair value investors ability to ascertain the fair market value of the instruments traded. It also intentionally and unduly complicates order execution. Quote stuffing is roughly equivalent to driving a race car at 190 miles per hour, but preventing the other drivers from exceeding 160 miles per hour. By clogging a trading venue s outbound quotation system (or inbound order entry systems) with near worthless quotes, astute HFT practitioners can execute trades on another trading venue or on the same trading venue with some degree of confidence that at least a plurality of market participants (including many other high frequency traders) will, at best, be reacting to delayed quotes, creating an arguably unfair trading advantage for these HFT practitioners that can slow down the other traders by relatively increasing their own reaction times. 19 As CNBC noted in 2012, the ultimate goal of many of these programs is to gum up the system so it slows down the quote feed to others and allows the computer traders (with their colocated servers at the exchanges) to gain a money-making arbitrage opportunity. 20 Price transparency is considered a public benefit of organized financial markets. It is difficult to envision that the practice of intentionally slowing down the dissemination of trade prices to the public is an activity that serves the public interest. The thesis of this paper is that, rather than attempting to ban these techniques (which could likely be difficult to enforce in practice), one could change the character and economics of the trading environment so as to disincentivize these and similar undesirable trading techniques. Rather than propose solutions that might preclude specific HFT strategies, we propose to simply change the economics of the trading environment by modifying the criteria of order allocation priority and by discouraging certain questionable industry 19 Quote stuffing is an offensive tool that high speed traders most typically use to gain a competitive advantage over other high speed traders. Click traders would not likely be adversely affected if outbound quotations were intentionally delayed by, say, 200 milliseconds, nor would they likely even be able to detect any such delay. 20 John Melloy, 2012, Mysterious algorithm was 4% of trading activity last week," CNBC, October 8, available at 9

10 practices to strike a more equitable balance between the high frequency trading community and the investment management community. Recommendations The proposal consists of nine recommendations that should be deemed as one complete set that should be considered and implemented as a whole, where appropriate. Several of the recommendations are admittedly rather complex, but so are the current electronic market structures in which we find ourselves. Our recommendations follow. 1. Trade Allocation with Cardinal Weighting of Time in the Order Book The ideal trade allocation algorithm should be a combination of the Pro Rata trade allocation algorithm and the Price/Time or FIFO trade allocation algorithm. 21 Descriptions of the Price/Time and Pro Rata algorithms would seem to be in order. Price/Time or FIFO The Price/Time trade allocation algorithm is also known as the FIFO algorithm. The Price/Time trade allocation algorithm first prioritizes all bids and orders based on price, and within price, prioritizes orders (in an ordinal ranking) based on the time that each order was received. An order can always gain priority by bettering its price, while keeping its original time stamp. Within the best price, the Price/Time algorithm attempts to completely fill the order with the oldest time stamp, (the lowest ordinal ranking) with any residual contracts or shares subsequently allocated to the next oldest bid or offer, until the appropriate contracts or shares have been fully allocated. The Price/Time trade allocation algorithm was the first algorithm utilized when the era of electronic trading was ushered in. Some market participants erroneously think that electronic markets still utilize the Price/Time trade 21 There are at least half a dozen other trade allocation algorithms currently in use but not specifically referenced in this section. While it might be quite valuable for interested market participants to have a detailed treatise on the various trade allocation algorithms currently in use, that is not the objective of this section. 10

11 allocation exclusively that is, that there are no other trade allocation algorithms. While it is equitable, some trading venues have diversified away from the Price/Time trade allocation as market participants tend to feel disconnected when they join the bid or offer, but are not senior enough to participate in any trade allocation. If there is a valid criticism of the Price/Time trade allocation algorithm, it is that it allocates trades based only on a simple ordinal ranking of bids or offers based on their respective time stamps. Basing the allocation of trades on a cardinal weighting (ranking) of trades based on their actual time stamps would seem to be a superior approach. Pro Rata In the Pro Rata trade allocation algorithm, all bids are allocated their pro rata share of the allocation of a matched trade based solely upon the lot size of their respective resting bid relative to the aggregate sum of all of the resting bids at the same price. For example, if there are a total of 2200 lots bid for at 12 and 220 offers hit the bid, each resting bid would be allocated 10% of the lot size for which they were bidding. 22 If there is a criticism of the Pro Rata trade allocation logic, it is that many market participants are constantly bidding or offering unrealistically large quantities, often far greater than they could likely realistically absorb. NYSE/Liffe NYSE/Liffe has a hybrid trade allocation algorithm that assigns resting bids and offers with an ordinal ranking (based on their time stamp) and then allocates trades based on a combination of the Pro Rata approach and the ordinal ranking of the bids and offers. In the formula that is equation (1), the first bracketed expression simply says that a market participant should be allocated the lesser of 1) the full amount of the quantity of his order or 2) a lesser quantity based upon where his respective order ranks in the order book, based on its time stamp, relative to 22 This trade allocation process has been shown to be prone to the apparently unavoidable rounding error chicanery that occurs when dozens and dozens of one lot orders are intentionally entered by a single market participant, in the hope of being unjustly enriched by the trading system rounding of what would have been anything greater than 51/100ths of a futures contract or share to one full contract or share. See recommendation 4. 11

12 the time stamps of the other orders in the order book. The second bracketed expression determines the pro rata quantity of any given order relative to the aggregate quantity of orders at the same price. The third bracketed expression determines the ordinal ranking (by time stamp) of any given order relative to the time stamps of all of the other orders at the same price in the order book. It is this third bracketed expression that we believe could be improved. NYSE/Liffe Time Pro-Rata algorithm 23 A Min vn, n n * N f r1 f r L where, v n ( N 1) n fn * (1) N N v r r r1 r1 N - Total number of resting buy (sell) orders sorted by time, n = 1(oldest) to N (newest) n - Individual order being considered r - Ascending sequence, 1 to N A n - Allocation for resting buy (sell) order, n v n - Volume of resting order being considered, n f n - Time Pro Rata Factor calculated for resting buy (sell) order being considered, n L - Incoming sell (buy) order volume Recommended Trade Allocation Algorithm When allocating trades, the instant proposal places a greater weighting on the time that an order is exposed to market risk. We extrapolate from the NYSE/Liffe model and assign a cardinal ranking, rather than an ordinal one, Fractional allocations are rounded down to the nearest integer for all allocations greater than 1 and rounded up to 1 for all fractional allocations less than 1. For equally sized fractional allocations, priority is granted to the oldest order. If any volume remains unallocated following this sequence (for instance, as a result of rounding or when the calculated allocation for an order is constrained by the Min function in the NYSE/Liffe Time Pro-Rata algorithm), then a further pass of the sequence will occur. 24 Ordinal ranking of resting orders involves creating a simple ranking, not unlike athletes who finish first, second or third in an athletic contest. That is, no consideration is given to the difference in athletic performance between the first and second finisher and the second and third finisher. A cardinal ranking would involve assigning a numeric value to the performances of the athletes, not unlike in gymnastics, where there is a quantitative evaluation of individual performances. In the instant proposal, our recommendation is to allocate matched trades based on the actual time that the orders have been resting in the order book relative to the times that 12

13 to resting bids and offers based on the actual length of time that bids and offers have been resting in the order book, relative to the time that all of the other orders have been resting in the order book. This is accomplished by raising time in the order book (Tau) to a low but effective exponential power A Min vn, n n * N f ' r1 f ' r L where, f ' n vn n * N N v r r r1 r1 (2) - Time duration (in milliseconds) for every resting order (time difference between the time of trade match and an incoming order s time stamp) N - Total number of resting buy (sell) orders sorted by time, n = 1(oldest) to N (newest) n - Individual order being considered - A constant parameter set by the trading venue A n - Allocation for resting buy (sell) order, n v n - Volume of resting order being considered, n f ' n - Proposed Time Pro Rata Factor calculated for resting buy (sell) order being considered, n L - Incoming sell (buy) order volume Note that equation (2) is identical to equation (1) except for the third bracketed expression. Whereas the third bracketed expression in (1) is based on a simple ordinal ranking of time in the order book, the third bracketed expression in (2) raises time in the order book causes a nonlinear marginal increase in the number of lots a longer-duration order is allocated, compared with a shorter-duration order, based on the time that the order has been exposed to market risk. This additional weighting (resulting in a greater allocation of trades) could be set by the trading venue by Figure 1 illustrates the progressive trade allocation results when is set equal to or greater than zero and less than or equal to 2.3. As is set to increasingly higher values, the weight associated with Tau (time in the order book) other orders have been resting in the order book and not based on the ordinal ranking of the respective time stamps of resting orders. Thus, in a cardinal ranking structure, an order that has been resting in the order book for four hours would be entitled to a far greater allocation of trades than an order that has been resting in the order book for only four seconds. In an ordinal ranking system, those orders would simply be ranked as #1 and #2 in priority. 13

14 increases exponentially and the actual time that an order has been resting in the order book becomes an increasingly dominant component when the algorithm allocates trades. It may be helpful to think of this recommendation as the introduction of perfect gradient shades of gray that lie between black (Pro Rata) and white (Price/Time). Market View Top of Book Limit Orders Listing all Bids at price of Allocations Figure 1 (α: 0 2.3) The horizontal axis reflects the value of. The vertical axis reflects the quantities (lots) that would be allocated to resting orders based upon their respective time in the order book. set to zero, each of the five resting bids would be allocated 60 lots, that is, an exact Pro Rata trade allocation (where time in the order book means nothing). As time in the order book receives more and more weighting. If 14

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